The Origins of Mimicry Rings

in Artificial Life VIII, Standish, Abbass, Bedau (eds)(MIT Press) 2002. pp 186{191 1 The Origins of Mimicry Rings Daniel W. Franks and Jason Noble School of Computing, University of Leeds Email: [dwfranks|jasonn]@comp.leeds.ac.uk Abstract Although beneficial to the mimic, Batesian mimicry is Mutualistic Mullerian¨ mimicry and parasitic Batesian detrimental to the survival of the model, as the presence mimicry can co-exist in mimicry rings, i.e., mimetic re- of tasty individuals dilutes the honesty of the warning lationships between multiple species. Theory suggests coloration. This means that Batesian mimicry is a par- that all Mullerian¨ mimics in an ecosystem should con- asitic relationship between mimic and model (see e.g., verge into one large ring. Potentially, the presence of Wickler, 1968) . Batesian mimics will encourage convergence. It has been suggested that rare species should seek out common Predators have fallible sensory systems and they gen- species as models. Mimicry rings have not previously eralize from their predation experiences. Thus, although been modelled; we present an evolutionary simulation to they are much more likely to mistake a perfect mimic investigate the above questions. Complete convergence for its model, they can still mistake approximate resem- is not observed although Batesian mimicry is shown to blances for the real thing. This gives a greater level of be important factor in the origin of mimicry rings. protection to a mimic the more accurate its mimicry be- Bees and wasps both possess yellow and black striped comes. However, if the appearances of two prey species warning colourations, making an obvious display of their are sufficiently distinct, the predator will never mistake unpleasantness. But why do they display the same pat- one for the other. Therefore, before mimics can undergo tern? Imagine a situation where they both have differ- a process of pattern refinement, an initial resemblance in ent patterns. A predator, such as a bird, would have to the eyes of the predator is needed at first so that it may sample many of each species in order to learn to avoid occasionally confuse the two. An approximate resem- them both. However, when sharing the same pattern, blance can be arrived at due to random drift, or fairly the predator will see them both as the same type of large major mutations (Turner 1988). prey and so will eat fewer individuals before develop- Population sizes have a considerable effect on mimicry. ing an aversion to them. Thus, each individual has less When two unpalatable species converge as Mullerian¨ chance of being eaten as it is protected by numbers. It mimics, they are doing so in order to increase the pop- therefore makes sense that two unpalatable (bad-tasting) ulation size of individuals with their type of warning species would become co-mimics by converging upon the coloration. It follows, then, that unpalatable species same colour pattern. This is assuming that the predator with higher population sizes are better defended against must learn which prey types to avoid, rather than hav- predators than those with lower population sizes (posi- ing innate aversions | an assumption in line with past tive frequency dependence). Conversely, Batesian mim- mimicry research. This type of relationship, in which ics are better protected by lower population sizes. This both species benefit, is known as Mullerian¨ mimicry is because higher populations would dilute the model's (Muller¨ 1879). protection so much as to make predators more willing The honest displays of unpalatability given by species to risk eating a prey of that colour pattern (negative such as bees and wasps are ready targets for free-riders. frequency dependence) (Pilecki & O'Donald 1971). A good example is the harmless hoverfly, which has black The coevolutionary dynamics involved in the two sep- and yellow stripes. It is easy to imagine the benefit arate cases of mimicry differ considerably (Turner 1987; gained by a tasty species in being confused for an un- 1995). Mullerian¨ mimics converge upon the same colour palatable one. So, gaining protection without evolving a pattern. That is, they `move' together serving both as costly toxin or sting, the species evolves the same warn- co-mimics and models. Batesian mimicry is a different ing colour as an unpalatable species. The species that matter. A Batesian mimic's colour pattern `moves' to- is being mimicked is termed \the model". This type ward that of the model's in order to gain protection. The of mimicry was the first to be described in the litera- model then attempts to escape the parasitic mimic by ture and is known as Batesian mimicry (Bates 1862). evolving its pattern àway' from the invading mimic, as 2 in Artificial Life VIII, Standish, Abbass, Bedau (eds) (MIT Press) 2002. pp 186{191 individuals that are slightly less parasitized (due to mi- 1. All of the Mullerian¨ mimics in a given ecosystem nor pattern differences) are more likely to survive. The should eventually converge into one large ring in order Batesian mimic is, however, expected to usually keep up to gain maximum protection; in the resulting coevolutionary arms race (Nur 1970). This is because the selection pressure on a tasty species 2. If the Mullerian¨ mimics do not converge into one large to gain protection is generally greater than the selec- ring, then the presence of Batesian mimics could entice tion pressure on a model to reduce the dilution of preda- them to do so, by adverging upon the rings; tor aversions. This type of mimetic arms race has been termed advergence (Brower, Brower, & Collins 1972). Although there are many mathematical and stochastic models of mimicry in the biological literature, this topic has not yet received any attention in the artificial life literature, apart from the authors' own previ- ous work (Franks & Noble 2002). Mimicry rings have hardly been explored at all in any literature, mainly due to the difficulty that a mathematical or stochastic model would have in modeling the large and complex ecosystem that would be required. This is where evolving artificial life models become the most practical and promising ap- proach. To that end, we present a simulation model that explores the evolution of mimicry rings under varying conditions. The Simulation Model Figure 1: Mimicry ring example. Three separate species To model prey we needed to create populations of of butterfly that all share the same pattern in a `yel- àgents' which each have an appearance and palatibil- low' ring in Sirena. Note the subtle differences in the ity level. Multiple populations of prey species were used patterns. in each experiment. Different species of prey were each assigned a fixed palatibility level on a scale between zero and one (least to most palatable), where 0.5 is neutrally Mimicry rings are Mullerian¨ mimetic relationships be- palatable. Each individual had two genes with values tween two or more species, and are common among but- compositely representing their external appearance (e.g., terflies and bumblebees. It is called a \ring" because a colour pattern) or phenotype.1 Both genes were con- each species in the relationship has an effect on each of strained to values from 1{200. The Euclidean distance the others. Plowright (1980) showed that there are five of one phenotype from another represented their level different patterns of bumblebee in north-west Europe, of similarity. Palatable species have values greater than each constituting a mimicry ring of several species. A 0.5, and unpalatable species have values lower than 0.5. good example of a mimicry ring is the `tiger' pattern A population of abstract predators was modelled with shared by different species of Heliconius butterflies in a Monte Carlo reinforcement learning system, as used in Sirena (Mallet & Gilbert 1995), alongside other ring pat- a stochastic model by Turner et. al. (1984). The preda- terns (see, e.g., Figure 1). It would be highly profitable tor's experience of each phenotype was represented by a for a palatable species to invade these rings as a Batesian score of attack probability, which was initialized to am- mimic, as it would have less of a dilution affect on the bivalence at 0.5. After eating prey of a particular phe- palatibility associated with the colour pattern. notype, the predator would make a post-attack update The co-existence of mimicry rings in nature has of the relevant probability according to the palatibility prompted the question: why do the co-mimics (in the of the individual consumed. The predator would use its same geographical region) not all converge into one large experience of different prey appearances to help it decide ring (Mallet & Gilbert 1995)? This question is posed be- on whether or not to attack them at the next opportu- cause all of the mimics would mutually, and optimally, nity. Unlike the stochastic model, the predator general- benefit from evolving into one large ring. The authors ized on the basis of experience and thus would also, to have previously shown (Franks & Noble 2002) in a sim- a lesser extent, update its scores for the closest neigh- ple, one-dimensional, three-species case that Batesian bouring phenotypes accordingly. The formula used for mimics can `chase' unpalatable species to within con- 1 vergence range of each other, provoking a Mullerian¨ re- Clearly a two-dimensional representation of possible phe- lationship. This suggests a potential for Batesian mimics notypes is a simplification. We have also looked at the evolution of mimicry rings in higher dimensional spaces; similar to influence the dynamics of mimicry rings. conclusions are reached but there are significant difficulties The above points suggest two working hypotheses: in visualizing and conveying the results.

Load more